[1]HUA Xiaopeng,SUN Yike,DING Shifei.An improved projection twin support vector machine[J].CAAI Transactions on Intelligent Systems,2016,11(3):384-389.[doi:10.11992/tis.201603049]
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An improved projection twin support vector machine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 384-389 Column: 学术论文—机器学习 Public date: 2016-06-25
Title:
An improved projection twin support vector machine
Author(s):
HUA Xiaopeng1;  SUN Yike1;  DING Shifei2
1. School of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China;
2. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Keywords:
classification; projection twin support vector machine; local information; weighted mean; neighborhood graph; quadratic programming; constraint condition; time complexity
CLC:
TP391.4
DOI:
10.11992/tis.201603049
Abstract:
A supervised classification method having a local learning ability, called weighted projection twin support vector machine (WPTSVM), is proposed. This method aims to improve upon a defect that projection twin support vector machines (PTSVMs) have, namely, that PTSVMs do not take account of the local structure and local information of a sample space in the training process. Compared with PTSVM, WPTSVM improves its local learning ability to some extent by attaching different weights for each sample according to the within-class neighborhood graph and replacing the standard mean with a weighted mean. Moreover, to reduce computational complexity, WPTSVM chooses a small number of boundary points in the contrary-class based on the between-class neighborhood graph to construct constraints of the original optimization problems. The method inherits the merits of PTSVM and can be regarded as an improved version of PTSVM. Experimental results on artificial and real datasets indicate the effectiveness of the WPTSVM method.
References:
[1] 皋军, 王士同, 邓赵红. 基于全局和局部保持的半监督支持向量机[J]. 电子学报, 2010, 38(7): 1626-1633. GAO Jun, WANG Shitong, DENG Zhaohong. Global and local preserving based semi-supervised support vector machine[J]. Acta electronica sinica, 2010, 38(7): 1626-1633.
[2] 花小朋, 丁世飞. 局部保持对支持向量机[J]. 计算机研究与发展, 2014, 51(3): 590-597.HUAxiaopeng, DING Shifei. Locality preserving twin support vector machines [J]. Journal of computer research and development, 2014, 51(3): 590-597.
[3] DING Shifei, HUA Xiaopeng, YU Junzhao. An overview on nonparallel hyperplane support vector machines[J]. Neural computing and applications, 2014, 25(5): 975-982.
[4] JAYADEVA, KHEMCHAND R, CHANDRA S. Twin support vector machines for pattern classification [J]. IEEE transaction on pattern analysis and machine intelligence, 2007, 29 (5): 905-910.
[5] MANGASARIAN O L, WILD E W. MultisurFace proximal support vector machine classification via generalized eigenvalues [J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28 (1): 69-74.
[6] PENG Xinjun,XU Dong. Bi-density twin support vector machines for pattern recognition[J]. Neurocomputing, 2013, 99: 134-143.
[7] CHEN Xiaobo, YANG Jian, YE Qiaolin, et al. Recursive projection twin support vector machine via within-class variance minimization[J]. Pattern recognition, 2011, 44 (10/11): 2643-2655.
[8] SHAO Yuanhai, WANG Zhen, CHEN Weijie, et al. A regularization for the projection twin support vector machine[J]. Knowledge-based systems, 2013, 37 (1): 203-210.
[9] YANG Xubing, CHEN Songcan, CHEN Bin, et al. Proximal support vector machine using local information [J]. Neurocomputing, 2009, 73(1): 357-365.
[10] COVER T M, HART P E. Nearest neighbor pattern classification[J]. IEEE transactions on information theory, 1967, 13 (1): 21-27.
[11] WANG Xiaoming, CHUNG Fulai, WANG Shitong. On minimum class locality preserving variance support vector machine[J]. Patter recognition, 2010, 43(8): 2753-2762.
[12] YE Qiaolin, ZhAO Chunxia, YE Ning, et al. Localized twin svm via convex minimization[J]. Neurocomputing, 2011, 74(4): 580-587.
[13] 皋军, 黄丽莉, 王士同. 基于局部子域的最大间距判别分析 [J ]. 控制与决策, 2014, 29 (5): 827-832.GAO Jun, HUANG Lili, WANG Shitong. Local sub-domains based maximum margin criterion [J]. Control and decision, 2014, 29 (5): 827-832.
[14] 邓乃杨, 田英杰. 支持向量机—理论、算法与拓展[M]. 北京: 科学出版社, 2009: 164-223.
[15] 丁立中, 廖士中. KMA-a: 一个支持向量机核矩阵的近似计算算法[J]. 计算机研究与发展, 2012, 49(4): 746-753.DING Lizhong, LIAO Shizhong. KMA-a: a kernel matrix approximation algorithm for support vector machines [J]. Journal of computer research and development, 2012, 49(4): 746-753.
[16] XUE Hui, CHEN Songchan. Glocalization pursuit support vector machine [J].Neural computing and applications, 2011, 20(7):1043-1053.
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